The Unit Economics of AI Labor

TL;DR

• A junior human FTE typically costs $95,000-$110,000 per year when fully loaded, based on industry loaded-cost ratios.
• Digital workers eliminate hidden costs like management overhead, attrition, and ramp time.
• Scaling digital workers is sub-linear, not linear like human hiring.
• The consistency of digital workers reduces costly operational errors.

Contents

• The true cost of a human FTE
• The cost of a digital worker
• Where the math works (and where it doesn't)
• The scaling advantage
• The hidden cost most people miss
• What to do next

Everyone talks about AI replacing jobs. Almost nobody talks about what AI labor actually costs.

This matters because the decision to deploy digital workers isn't philosophical. It's financial. And the financial case is stronger than most people realize, once you account for the costs that don't show up on a salary line.

The true cost of a human FTE

Start with the obvious number. A junior operations specialist in the US costs $55,000 to $70,000 in base salary [1]. But that's not the real cost.

The loaded cost includes:

• Benefits and taxes: Add 25-35%. Health insurance, 401(k) match, payroll taxes, workers' comp. Per the U.S. Bureau of Labor Statistics, benefits average roughly 30% of total compensation for private-industry workers [2]. That $60,000 salary is really closer to $78,000 once benefits and payroll taxes are in.
• Management overhead: Someone has to manage this person. Weekly 1:1s, performance reviews, task assignment, quality checks. Budget 10-15% of a manager's time per direct report.
• Tools and infrastructure: Laptop, software licenses, desk space (if on-site). $5,000-$15,000 per year depending on your stack.
• Ramp time: It takes 2-4 months for a new hire to reach full productivity. During that time, they're producing at 40-60% capacity while consuming 100% of their cost.
• Attrition cost: Operations and admin roles typically turn over every 18-24 months in the US labor market, consistent with BLS JOLTS separation rates for professional and business services [3]. SHRM and Gallup research estimates replacement cost at 50-200% of annual salary when recruiting, onboarding, and lost productivity are included [4].

When you add it up, a $60,000 ops hire commonly lands in the $95,000-$110,000 range per productive year once benefits, overhead, tools, ramp, and attrition are priced in. They work about 1,800 hours of that year (after PTO, holidays, sick days, meetings, and context switching).

That's roughly $55-$61 per productive hour for repeatable operations work.

The cost of a digital worker

A digital worker's cost structure is fundamentally different. There's no salary. The costs are:

• Compute: LLM inference, API calls, data processing. For a worker handling lead qualification and routing, this runs $200-$800 per month depending on volume.
• Orchestration infrastructure: The platform that manages the worker, handles scheduling, retries, escalations, and audit logging. At Poly, this is part of the platform cost.
• Setup and configuration: One-time cost to define the workflow, success criteria, and escalation rules. Typically 2-5 days of implementation time.
• Monitoring: Someone reviews the worker's output periodically. At steady state, this is 1-2 hours per week for a well-configured worker.

For a typical mid-volume operations workflow (processing 500-2,000 items per month), the all-in cost of a digital worker typically runs a fraction of an FTE, well under $2,000/month for most mid-volume workflows. Compare that to the $95,000-$110,000 loaded cost of the human doing the same work.

Where the math works (and where it doesn't)

Digital workers win on cost, speed, and consistency for work that is:

• Repeatable. The same general process runs hundreds of times per month.
• Rule-definable. Success criteria can be specified clearly. "Lead is qualified if it has a valid email, company size over 10, and industry in our ICP."
• Multi-system. The work crosses multiple tools (CRM to email to calendar to billing).
• Time-sensitive. Speed matters. A lead that waits 4 hours for routing is worth less than a lead routed in 4 minutes.

Digital workers are not the right choice for:

• Relationship-heavy work. Closing a $500,000 deal requires human judgment, empathy, and negotiation that AI can't replicate.
• Novel problem-solving. If every instance is unique, there's no pattern to automate.
• Creative strategy. Setting the direction requires human vision. Executing the direction is where workers excel.

The scaling advantage

Here's where the economics really separate.

Scaling a human team is linear. Double the work, double the headcount. Each new hire adds the same $95,000-$110,000 in loaded costs, plus increased management complexity.

Scaling digital workers is sub-linear. Double the work and your compute costs increase by 60-70%, not 100%. No new management overhead. No recruiting timeline. No ramp period.

For an agency doing $1M in revenue, this difference is meaningful. For an agency trying to get from $1M to $5M without hiring proportionally, it's the entire strategy.

The hidden cost most people miss

The biggest cost of human operations work isn't the salary. It's the error rate.

A human processing 200 lead qualification decisions per day makes mistakes. Fatigue, distraction, inconsistency. Even at 95% accuracy, that's 10 misrouted leads per day. Over a month, that's 200 leads that went to the wrong rep, got the wrong follow-up, or fell through the cracks entirely.

A digital worker running the same rules produces the same output every time. Not because it's smarter. Because it doesn't get tired, distracted, or have a bad Monday.

The consistency advantage compounds. Better routing means faster response times. Faster response times mean higher conversion rates. Higher conversion rates mean more revenue per lead. The unit economics cascade.

What to do next

Pull up your highest-volume operations workflow. The one where someone on your team spends 10+ hours per week doing the same process with minor variations.

Calculate the loaded cost. Include the error rate. Include the management time. Include what happens when that person quits and you need to retrain someone.

Then ask: would a digital worker that costs a fraction of that amount, runs 24/7, and never makes a consistency error be a better allocation of capital?

For most teams, the answer is obvious once you see the numbers.

Book a Poly Workforce Strategy Call and we'll run the unit economics analysis for your specific workflow.

Sources

1. U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics — Business Operations Specialists. https://www.bls.gov/oes/current/oes131199.htm
2. U.S. Bureau of Labor Statistics, "Employer Costs for Employee Compensation." https://www.bls.gov/news.release/ecec.nr0.htm
3. U.S. Bureau of Labor Statistics, Job Openings and Labor Turnover Survey (JOLTS). https://www.bls.gov/jlt/
4. SHRM, "Essential Elements of Employee Retention" (replacement cost research summary). https://www.shrm.org/topics-tools/news/talent-acquisition/essential-elements-employee-retention
5. Gallup, "This Fixable Problem Costs U.S. Businesses $1 Trillion." https://www.gallup.com/workplace/247391/fixable-problem-costs-businesses-trillion.aspx